Novel Metabolic Signatures of Prostate Cancer Revealed by 1H-NMR Metabolomics of Urine

Yang, Bo; Zhang, Chuan; Cheng, Sheng; Li, Gonghui; Griebel, Jan; Neuhaus, Jochen

doi:10.3390/diagnostics11020149

Cited by 27 publications

(15 citation statements)

References 64 publications

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“…Various studies have shown that urine NMR metabolomics has potential as a screening tool for accurate diagnosis [ 26 , 27 , 28 ]. Since, to the best of our knowledge, no investigation has been performed so far on the urine metabolome of BAV subjects, in the present study we applied urine-based metabolomics for a comparative analysis of the metabolic profile of adult asymptomatic subjects with BAV ( n = 20) and healthy controls ( n = 24).…”

Section: Discussionmentioning

confidence: 99%

Urinary Metabolomics Study of Patients with Bicuspid Aortic Valve Disease

et al. 2021

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Bicuspid aortic valve (BAV) is the most common congenital heart defect responsible for valvular and aortic complications in affected patients. Causes and mechanisms of this pathology are still elusive and thus the lack of early detection biomarkers leads to challenges in its diagnosis and prevention of associated cardiovascular anomalies. The aim of this study was to explore the potential use of urine Nuclear Magnetic Resonance (NMR) metabolomics to evaluate a molecular fingerprint of BAV. Both multivariate and univariate statistical analyses were performed to compare the urinary metabolome of 20 patients with BAV with that of 24 matched controls. Orthogonal partial least squared discriminant analysis (OPLS-DA) showed statistically significant discrimination between cases and controls, suggesting seven metabolites (3-hydroxybutyrate, alanine, betaine, creatine, glycine, hippurate, and taurine) as potential biomarkers. Among these, glycine, hippurate and taurine individually displayed medium sensitivity and specificity by receiver operating characteristic (ROC) analysis. Pathway analysis indicated two metabolic pathways likely perturbed in BAV subjects. Possible contributions of gut microbiota activity and energy imbalance are also discussed. These results constitute encouraging preliminary findings in favor of the use of urine-based metabolomics for early diagnosis of BAV.

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Section: Discussionmentioning

confidence: 99%

Urinary Metabolomics Study of Patients with Bicuspid Aortic Valve Disease

et al. 2021

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“…Before the data analysis, we checked the data integrity. The normalization of the soil variables was carried out using the statistical package in R software version 4.0.2 (R Core Team, Austria) [ 44 ] based on the geometric mean, and a generalized logarithmic transformation using “glog” function in R was performed to make the variables comparable among themselves due to differences in the units to measure them [ 45 , 46 ]. Figure 8 shows the general scheme of the data analysis procedures followed in this work.…”

Section: Methodsmentioning

confidence: 99%

Identification of Soil Properties Associated with the Incidence of Banana Wilt Using Supervised Methods

Olivares

Vega

Calderón

et al. 2022

Plants

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Over the last few decades, a growing incidence of Banana Wilt (BW) has been detected in the banana-producing areas of the central zone of Venezuela. This disease is thought to be caused by a fungal–bacterial complex, coupled with the influence of specific soil properties. However, until now, there was no consensus on the soil characteristics associated with a high incidence of BW. The objective of this study was to identify the soil properties potentially associated with BW incidence, using supervised methods. The soil samples associated with banana plant lots in Venezuela, showing low (n = 29) and high (n = 49) incidence of BW, were collected during two consecutive years (2016 and 2017). On those soils, sixteen soil variables, including the percentage of sand, silt and clay, pH, electrical conductivity, organic matter, available contents of K, Na, Mg, Ca, Mn, Fe, Zn, Cu, S and P, were determined. The Wilcoxon test identified the occurrence of significant differences in the soil variables between the two groups of BW incidence. In addition, Orthogonal Least Squares Discriminant Analysis (OPLS-DA) and the Random Forest (RF) algorithm was applied to find soil variables capable of distinguishing banana lots showing high or low BW incidence. The OPLS-DA model showed a proper fitting of the data (R2Y: 0.61, p value < 0.01), and exhibited good predictive power (Q2: 0.50, p value < 0.01). The analysis of the Receiver Operating Characteristics (ROC) curves by RF revealed that the combination of Zn, Fe, Ca, K, Mn and Clay was able to accurately differentiate 84.1% of the banana lots with a sensitivity of 89.80% and a specificity of 72.40%. So far, this is the first study that identifies these six soil variables as possible new indicators associated with BW incidence in soils of lacustrine origin in Venezuela.

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“…However, in the Cao et al study investigating sarcosine levels in urine supernatant and sediment, the creatine-and alaninenormalized sarcosine levels were statistically higher in PCa patients than in abnormal prostate without cancer patients or healthy patients, from both sample source and normalization protein [123]. A very recent study using a PCa urine-based 1 H-NMR revealed that guanidinoacetate, phenylacetylglycine, and glycine were significantly increased while L-lactate and L-alanine were substantially decreased [124]. In the 20 metabolites identified, sarcosine was not even a player in PCa after employing principal component analysis (PCA), partial least squares-differential analysis (PLS-DA), ortho-PLS-DA (OPLS-DA), and the Wilcoxon test.…”

Section: Why Focus On Metabolomics For Pca Cancer Research?mentioning

confidence: 96%

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Resurreccion

Fong

2022

Metabolites

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Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

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Novel Metabolic Signatures of Prostate Cancer Revealed by 1H-NMR Metabolomics of Urine

Cited by 27 publications

References 64 publications

Urinary Metabolomics Study of Patients with Bicuspid Aortic Valve Disease

Urinary Metabolomics Study of Patients with Bicuspid Aortic Valve Disease

Identification of Soil Properties Associated with the Incidence of Banana Wilt Using Supervised Methods

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

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